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. 2025 Dec;35(12):7834-7844.
doi: 10.1007/s00330-025-11735-6. Epub 2025 Jun 6.

Beyond nodules: body composition as a biomarker for future lung cancer

Jing Wang  1 , Joseph K Leader  1 , Xin Meng  1 , Tong Yu  1   2 , Renwei Wang  3 , Jian-Min Yuan  3   4 , David Wilson  3   5 , Jiantao Pu  6   7   8   9
Affiliations

Beyond nodules: body composition as a biomarker for future lung cancer

Jing Wang et al. Eur Radiol. 2025 Dec.

Abstract

Objectives: To investigate if body composition can serve as a biomarker for assessing the risk of developing lung cancer.

Materials and methods: We conducted a retrospective study using low-dose computed tomography (LDCT) scans from the Pittsburgh lung screening study (PLuSS) (n = 3635, 22 follow-up years) and the NLST-ACRIN (n = 16,360, 8 follow-up years) cohort. Five types of body tissues, including subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and bone, were automatically segmented by our previously developed algorithms. Volume and density metrics were computed. Cause-specific Cox proportional hazards models were utilized to assess hazard ratios (HRs). Time-dependent area under the receiver operating characteristic curve (AUC-ROC) was used to evaluate model performance. The cumulative incidence function was estimated for different risk groups.

Results: The final composite models were formed by age (HR = 1.30 (95% CI: 1.17-1.43)), current smoking status (HR = 1.85 (1.49-2.28)), bone volume (HR = 1.38 (1.25-1.52)), bone density (HR = 0.80 (0.71-0.89)), SM density (HR = 0.62 (0.58-0.66)), IMAT ratio (HR = 0.65 (0.58-0.73)), and SAT volume (HR = 0.76 (0.67-0.87)). The model trained on the PLuSS cohort achieved a mean AUC of 0.77 (0.75-0.79) over 21 years and 0.71 (0.68-0.74) over the first 7 years for lung cancer prediction. External validation on the NLST cohort yielded AUC values ranging from 0.63 to 0.66 over a 7-year follow-up period. The model trained on a combined dataset of PLuSS and NLST achieved a mean AUC of 0.71 (0.7-0.72) over 21 years.

Conclusion: Three-dimensional body composition metrics assessed through LDCT are a significant predictor of lung cancer risk.

Key points: Question Is body composition a biomarker for lung cancer risk assessment? Findings Body composition metrics derived from low-dose CT scans, including volumes and densities of fat, bone, and muscle, are strong predictors of lung cancer risk. Clinical relevance Lung cancer risk stratification can be improved by body composition features, providing guidance for personalized lung cancer screening strategies.

Keywords: Body composition; Early detection of cancer; Lung neoplasms; Risk assessment.

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Conflict of interest statement

Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Jiantao Pu. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was waived by the Institutional Review Board. Ethical approval: Institutional Review Board approval was obtained from the University of Pittsburgh (IRB: 21020128). Study subjects or cohorts overlap: Study subjects or cohorts have not been previously reported. Methodology: Retrospective Observational Multicenter study

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